Traditionally, person recognition systems operate by establishing a positive identification from facial features. Such systems are part of a class of one-dimensional recognizers that are based on static biometric identification data. Many face-detection-based solutions use offline deep learning techniques to create models from significant amounts of data, for example, available in cloud repositories. When using these models, the accuracy of the recognition is dependent on the quality of the training dataset (e.g., whether the frontal face, foreground, and/or silhouette are visible or have the same illumination or other characteristics) because deficiencies in the training data often reappear when the model is used for recognition.